A Study on Disturbance Suppression of Self-Tuning Generalized Predictive Control Method with Reduced Computational Complexity

Generalized Predictive Control (GPC) is one of the model-based control methods. The control law is derived by a performance index consisting of the error between the reference signal and the output prediction, and the sum of squares of the control inputs. In recent research, we proposed a new output prediction and extended the control law so that the disturbance response characteristics can be adjusted without changing the output response characteristics. However, there was a problem that the amount of calculation increased when using the self-tuning controller. Therefore, in this paper, we propose a method to reduce the computational complexity while enabling readjustment of the disturbance response characteristics without changing the closed-loop characteristics by adopting a new method to reduce the computational complexity of the control law. The effectiveness of the proposed method is demonstrated by numerical simulations.